Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34136377", "kind": "science-record", "revision": 7, "content_hash": "sha256:dae73962ca7a7cd7b40f26a915fc0e03b43a72093f23411ab15d18589718661e", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T22:03:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma.", "doi": "10.3389/fonc.2021.615963", "pmid": "34136377", "authors": ["Rujia Qin", "Wen Peng", "Xuemin Wang", "Chunyan Li", "Yan Xi", "Zhaoming Zhong", "Chuanzheng Sun"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein-protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify \"real\" hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.", "claims": [{"id": "claim-method", "statement": "P-values of DEGs were identified using a Wilcox test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34136377", "locator": "sections[3] \"Data Collection and Data Processing\" paragraph 2", "quote": "P-values of DEGs were identified using a Wilcox test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The datasets generated for this study can be found here: The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) datasets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54467).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34136377", "locator": "sections[23] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The datasets generated for this study can be found here: The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) datasets (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54467)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34136377"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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In conclusion, this study mi", "erential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might p"], "order_basis": "paper_text_order:1540", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tion and Data Processing The RNA sequencing (RNA-seq) data of 471 SKCM samples were downloaded fr", "re regarded as DEGs. 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